Fuel-efficient driving strategies
Doktorsavhandling, 2020

This thesis is concerned with fuel-efficient driving strategies for vehicles driving on roads with varying topography, as well as estimation of road grade and vehicle mass for vehicles utilizing such strategies. A framework referred to as speed profile optimization (SPO), is introduced for reducing the fuel or energy consumption of single vehicles (equipped with either combustion or electric engines) and platoons of several vehicles. Using the SPO-based methods, average reductions of 11.5% in fuel consumption for single trucks, 7.5 to 12.6% energy savings in electric vehicles, and 15.8 to 17.4% average fuel consumption reductions for platoons of trucks were obtained. Moreover, SPO-based methods were shown to achieve higher savings compared to the commonly used methods for fuel-efficient driving. Furthermore, it was demonstrated that the simulations are sufficiently accurate to be transferred to real trucks. In the SPO-based methods, the optimized speed profiles were generated using a genetic algorithm for which it was demonstrated, in a discretized case, that it is able to produce speed profiles whose fuel consumption is within 2% of the theoretical optimum.

A feedforward neural network (FFNN) approach, with a simple feedback mechanism, is introduced and evaluated in simulations, for simultaneous estimation of the road grade and vehicle mass. The FFNN provided road grade estimates with root mean square (RMS) error of around 0.10 to 0.14 degrees, as well as vehicle mass estimates with an average RMS error of 1%, relative to the actual value. The estimates obtained with the FFNN outperform road grade and mass estimates obtained with other approaches.

speed profile optimization

fuel efficiency

neural networks.

energy efficiency

genetic algorithms

performance analysis

road grade estimation

mass estimation

vehicle platooning

Opponent: Dr. Mirjana Ivanović, Department of Mathematics and Informatics Faculty of Sciences, University of Novi Sad, Serbia


Sina Torabi

Chalmers, Mekanik och maritima vetenskaper, Fordonsteknik och autonoma system

Truck Platooning Based on Lead Vehicle Speed Profile Optimization and Artificial Physics

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC 2015,; Vol. 2015 October(2015)p. 394-399

Paper i proceeding

Fuel consumption optimization of heavy-duty vehicles using genetic algorithms

2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings,; (2017)p. 29-36

Paper i proceeding

Fuel-Efficient Driving Strategies for Heavy-Duty Vehicles: A Platooning Approach Based on Speed Profile Optimization

Journal of Advanced Transportation,; Vol. 2018(2018)

Artikel i vetenskaplig tidskrift

A method for performance analysis of a genetic algorithm applied to the problem of fuel consumption minimization for heavy-duty vehicles

Applied Soft Computing Journal,; Vol. 80(2019)p. 735-741

Artikel i vetenskaplig tidskrift

Road grade and vehicle mass estimation for heavy-duty vehicles using feedforward neural networks

4th International Conference on Intelligent Transportation Engineering, ICITE 2019,; (2019)p. 316-321

Paper i proceeding

Energy minimization for an electric bus using a genetic algorithm

European Transport Research Review,; Vol. 12(2020)

Artikel i vetenskaplig tidskrift

The EU has set ambitious targets for CO2 emission reduction for the next decades,
aiming to reduce, by the year 2050, the CO2 emissions from all sources by 80 - 95%
relative to the 1990 level. However, as a consequence of the increase in transport demand,
the CO2 emissions from the transport sector have increased (in absolute terms) over the
past decades. In relative terms, road transport is currently responsible for 21% of the
total CO2 emissions in the EU.
Furthermore, considering that hauling companies typically own many vehicles that
each travel around 130,000 km per year on average, fuel consumption plays a major role.
In fact, fuel accounts for around one third of the total cost of ownership and operation
of conventional trucks. Thus, vehicle manufacturers as well as hauling companies are
seeking technologies that improve the energy efficiency of vehicles. Reducing the energy
consumption of vehicles even by a few per cent can translate to significant environmental
and economical benefits.
This thesis is focused on improving the efficiency of vehicles by introducing a novel
framework for energy-efficient driving. In this framework, vehicles follow a speed profile
(i.e. the speed variation over the road) that has been optimized for energy efficiency.
The methods presented here reduce the fuel consumption of conventional trucks by an
average of 11.5% and extend the range of electric minibuses by 7.5 to 12.6%.







Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4829


Chalmers tekniska högskola

Opponent: Dr. Mirjana Ivanović, Department of Mathematics and Informatics Faculty of Sciences, University of Novi Sad, Serbia

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